Search Results for author: Dahun Kim

Found 17 papers, 7 papers with code

Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering

no code implementations15 Sep 2021 Youngjoong Kwon, Dahun Kim, Duygu Ceylan, Henry Fuchs

To tackle this, we propose Neural Human Performer, a novel approach that learns generalizable neural radiance fields based on a parametric human body model for robust performance capture.

Learning Open-World Object Proposals without Learning to Classify

1 code implementation15 Aug 2021 Dahun Kim, Tsung-Yi Lin, Anelia Angelova, In So Kweon, Weicheng Kuo

In this paper, we identify that the problem is that the binary classifiers in existing proposal methods tend to overfit to the training categories.

Object Detection Object Discovery +1

Learning to Associate Every Segment for Video Panoptic Segmentation

no code implementations CVPR 2021 Sanghyun Woo, Dahun Kim, Joon-Young Lee, In So Kweon

Temporal correspondence - linking pixels or objects across frames - is a fundamental supervisory signal for the video models.

Panoptic Segmentation

DeepLab2: A TensorFlow Library for Deep Labeling

1 code implementation17 Jun 2021 Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, Daniel Cremers, Laura Leal-Taixe, Alan L. Yuille, Florian Schroff, Hartwig Adam, Liang-Chieh Chen

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.

The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation

no code implementations26 Nov 2020 Myungchul Kim, Sanghyun Woo, Dahun Kim, In So Kweon

In this work, we propose Boundary Basis based Instance Segmentation(B2Inst) to learn a global boundary representation that can complement existing global-mask-based methods that are often lacking high-frequency details.

Instance Segmentation Scene Understanding +1

Video Panoptic Segmentation

1 code implementation CVPR 2020 Dahun Kim, Sanghyun Woo, Joon-Young Lee, In So Kweon

In this paper, we propose and explore a new video extension of this task, called video panoptic segmentation.

Instance Segmentation Panoptic Segmentation +4

Preserving Semantic and Temporal Consistency for Unpaired Video-to-Video Translation

no code implementations21 Aug 2019 Kwanyong Park, Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon

In this paper, we investigate the problem of unpaired video-to-video translation.

Domain Adaptation

Deep Blind Video Decaptioning by Temporal Aggregation and Recurrence

1 code implementation CVPR 2019 Dahun Kim, Sanghyun Woo, Joon-Young Lee, In So Kweon

Blind video decaptioning is a problem of automatically removing text overlays and inpainting the occluded parts in videos without any input masks.

Video Denoising Video Inpainting +1

Discriminative Feature Learning for Unsupervised Video Summarization

no code implementations24 Nov 2018 Yunjae Jung, Donghyeon Cho, Dahun Kim, Sanghyun Woo, In So Kweon

The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance.

Supervised Video Summarization Unsupervised Video Summarization

Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles

no code implementations24 Nov 2018 Dahun Kim, Donghyeon Cho, In So Kweon

Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have been utilized for visual representation learning for still images, when the number of labeled images is limited or absent at all.

Colorization Representation Learning +1

LinkNet: Relational Embedding for Scene Graph

1 code implementation NeurIPS 2018 Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon

In this paper, we present a method that improves scene graph generation by explicitly modeling inter-dependency among the entire object instances.

Graph Generation Scene Graph Generation

Learning Image Representations by Completing Damaged Jigsaw Puzzles

no code implementations6 Feb 2018 Dahun Kim, Donghyeon Cho, Donggeun Yoo, In So Kweon

The recovery of the aforementioned damage pushes the network to obtain robust and general-purpose representations.

Colorization Representation Learning +2

Two-Phase Learning for Weakly Supervised Object Localization

no code implementations ICCV 2017 Dahun Kim, Donghyeon Cho, Donggeun Yoo, In So Kweon

Weakly supervised semantic segmentation and localiza- tion have a problem of focusing only on the most important parts of an image since they use only image-level annota- tions.

Weakly-Supervised Object Localization Weakly-Supervised Semantic Segmentation

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